shachen (沙尘)

Infrared satellite dust storm detection in Python — an open implementation of DEBRA-Dust, the Dynamic Enhancement with Background Reduction Algorithm (Miller et al. 2017, doi:10.1002/2017JD027365), for GOES ABI and Himawari AHI.

shachen (沙尘) is Chinese for "sand and dust". The package is a home for infrared-channel dust algorithms.

This appears to be the first public implementation of DEBRA.

All equations follow the erratum published 26 February 2020, which supersedes the 2017 print run for Eqs. 7, 21–22 and 24–25.

Dust is the yellow modulation; everything else stays in greyscale infrared. Left is the case from the paper's Figure 6. Both panels are produced by scripts/run_case.py, one per sensor.

What it does

DEBRA turns the split-window infrared signal that is specific to mineral dust into a per-pixel confidence field, by comparing each pixel against a dynamically estimated clear-sky background rather than a fixed threshold. Over bright, emissivity-heterogeneous desert surfaces, fixed thresholds produce false alarms; the dynamic background removes that dependence.

io.satellite.load_scene   L1b → bt_* / refl_* on the 2 km fixed grid (satpy)
        │
        ▼
pipeline.run_debra
├─ geo.regrid_latlon      MERRA-2 / CAMEL → satellite grid
├─ solar                  per-pixel solar zenith (day / twilight / night mix)
├─ background             scheme A: CAMEL emissivity × Planck(MERRA-2 skin T)
│    or composite         scheme B: 14-day cloud-cleared same-hour composite
├─ cloudmask              Eqs. 1–12, including the dust restoral term
├─ dust_tests             DT1–DT3, Eqs. 13–15, normalised per-pixel
├─ confidence             Eqs. 16–22 → cf_comb
├─ imagery                Eqs. 23–29, CF-modulated RGB
└─ render                 georeferenced PNG with coastlines (cartopy)

Only one background scheme is used at a time: run_debra requires exactly one of emissivity= or background=. The composite scheme carries the split-window water-vapour depression that the semi-analytic one lacks.

The second algorithm is the baseline the first is judged against: pipeline.run_dust_rgb is the classic Dust RGB (Lensky and Rosenfeld 2008; GOES-R Quick Guide) — three fixed infrared stretches, no background, no cloud mask. It needs no ancillary data, reads one band DEBRA does not (11.2 µm), and returns the same (y, x, gun) layout, so both render through the same path. scripts/run_case.py writes it beside every DEBRA image for comparison.

Its stretches are picked per sensor from the scene's reader, because the scheme has no single canonical set of numbers, having been re-tuned for each imager after SEVIRI. A baseline rendered here therefore matches that satellite's operational product. The Dust RGB page has the table and the references.

Documentation

https://ringsaturn.github.io/shachen/ — user guide, all 29 equations as implemented, full API reference, and the deviations page.

make -C docs html      # → docs/_build/html/index.html
make -C docs latexpdf  # → docs/_build/latex/shachen.pdf (needs a TeX install)

Install

The core install runs the entire algorithm on fields already in memory, and pulls in no I/O or plotting stack:

pip install shachen

Optional extras:

pip install "shachen[satellite]"   # satpy: read and calibrate ABI/AHI L1b
pip install "shachen[data]"        # earthaccess/s3fs: fetch MERRA-2, CAMEL, L1b
pip install "shachen[render]"      # cartopy/matplotlib: georeferenced PNG
pip install "shachen[all]"         # everything, for the reproduction scripts

import shachen pulls in none of the extras.

Usage

import shachen

result = shachen.run_debra(scene, emissivity=camel, skin_temperature=merra_ts)
result["cf_comb"]  # combined dust confidence, 0–1

baseline = shachen.run_dust_rgb(scene)  # the classic Dust RGB, for comparison
baseline["dust_rgb"]  # (y, x, gun) floats in 0–1

What scene must contain, the two background schemes, and the imagery chain are covered in the user guide.

Reproducing a reference case end to end needs [all] plus an Earthdata login in ~/.netrc (MERRA-2 and CAMEL are authenticated downloads; GOES L1b on AWS S3 is anonymous):

python scripts/fetch_case.py 2017-03-23-swus   # the paper's Figure 6 case
python scripts/run_case.py   2017-03-23-swus   # → netCDF + PNG

Deviations from the paper

Four printed equations are inconsistent with the paper's own prose, figures or internal scales even after the erratum, and are implemented per the prose:

Plus one substitution (CAMEL emissivity for the registration-walled UWBF) and one opt-in per-sensor retune.

Upgrading to 0.3.0

The Eq. 19 split changes cf_ngt, and through it cf_comb at night, for every caller — night confidences roughly double. ConfidenceConstants now takes cf_norm_day and cf_norm_ngt in place of cf_norm; passing cf_norm= still works and sets both to that one interval, which reproduces 0.2.x exactly:

ConfidenceConstants(cf_norm=Bounds(0.25, 2.50))   # pre-0.3 behaviour

Reading constants.confidence.cf_norm back now yields None (it is an InitVar), so cf_norm.min raises rather than silently normalizing with the wrong bounds. Read cf_norm_day / cf_norm_ngt.

All of it, with the reasoning and the numbers, is in docs/deviations.md. Read that before changing any of it. constants.py is the single source of every calibration bound, offset and weight from the paper, unit-tested against an independent transcription.

Citation

Cite the software itself from CITATION.cff (GitHub's "Cite this repository" button renders it). Which scientific references to add depends on which algorithm you ran: DEBRA and the Dust RGB are separate published schemes that share nothing but their input bands:

DEBRA-Dust, the algorithm this package implements:

Miller, S. D., Bankert, R. L., Grasso, L. D., Lindsey, D. T., Kuciauskas, A. P., & Combs, C. L. (2017). A dynamic enhancement with background reduction algorithm: Overview and application to satellite-based dust storm detection. Journal of Geophysical Research: Atmospheres, 122, 12,938–12,959. https://doi.org/10.1002/2017JD027365

Dust RGB, the comparison baseline, origin of the scheme:

Lensky, I. M., & Rosenfeld, D. (2008). Clouds-Aerosols-Precipitation Satellite Analysis Tool (CAPSAT). Atmospheric Chemistry and Physics, 8, 6739–6753. https://doi.org/10.5194/acp-8-6739-2008

...and the recipe applied, which differs by sensor: ABI scenes use the GOES-R Quick Guide's adjusted stretches, AHI scenes the original SEVIRI ones:

Fuell, K. (contributor). Quick Guide: Dust RGB. NOAA/NASA GOES-R, CIRA/RAMMB. https://rammb.cira.colostate.edu/training/visit/quick_guides/Dust_RGB_Quick_Guide.pdf

EUMeTrain. Compilation of RGB Recipes. https://eumetrain.org/sites/default/files/2020-05/RGB_recipes.pdf

Berndt et al. (2018) documents why those two differ; cite it if the per-sensor distinction matters to your result. All of these are in CITATION.cff with their scopes; the Dust RGB page shows which values go with which sensor.

This is an independent implementation. It is not produced, endorsed, or verified by the papers' authors, by EUMETSAT, by CIRA, or by NOAA.

License

Apache-2.0. See NOTICE for attribution requirements.